A machine learning approach to identify stride characteristics predictive of musculoskeletal injury, enforced rest and retirement in Thoroughbred racehorses.

ABSTRACT: Decreasing speed and stride length over successive races have been shown to be associated with musculoskeletal injury (MSI) in racehorses, demonstrating the potential for early detection of MSI through longitudinal monitoring of changes in stride characteristics. A machine learning (ML) approach for early detection of MSI, enforced rest, and retirement events using this same horse-level, race-level, and stride characteristic data across all race sectionals was investigated. A CatBoost model using features from the two races prior to an event had the highest classification performance (sensitivity score for MSI, enforced rest and retirement equal to 0.00, 0.58, 0.76, respectively and balanced accuracy score corresponding to 0.44), with scores decreasing for models incorporating windows that

SUBMITTER: Bogossian PM 

PROVIDER: S-EPMC11584853 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature